Non-player character (NPC) dialogue plays a crucial role in shaping the player experience in narrativedriven video games, influencing agency, immersion and story engagement. Despite the recent advancements in large language models (LLMs) for dynamic dialogue generation, few empirical studies have compared their impact across different dialogue system designs. This pilot study explores how LLM-driven dialogue systems affect the player experience using a custom-developed role-playing game (RPG) featuring four different dialogue designs; static control (CV), rephrase (A), hybrid (B) and fully open-ended (C). Behavioral data and post-game questionnaires were collected from 64 participants. Results indicate that fully open-ended dialogues led to significantly longer dialogue interactions and higher overall engagement, particularly among casual players, with the survey feedback highlighting its immersive and natural tone. These findings suggest that fully open-ended LLM-based dialogue in video games can enhance narrative depth and player involvement.

This study investigates player perceptions of conceptual Large Language Model (LLM)-driven Non-Player Characters (NPCs) in World of Warcraft (WoW), referred to as “AIBuddies.” Building on prior work on Conversational Artificial Autonomous Agents (CA-bots), it explores how such companions could enhance gameplay and player experience. A mixed-method survey of 273 WoW players was conducted, using visual mock-ups to present the AI-Buddy concept rather than a working prototype. Participants evaluated interaction features, customization options, and potential gameplay impact. Results show general support, particularly among Casual players, for using AI-Buddies to improve solo content, personalization, and immersion. However, concerns were raised about balance, multiplayer exploitation, and ethical implications. Thematic analysis also revealed worries about reduced social interaction and technical feasibility. This short study contributes to ongoing discussions on CA-bots and LLM-driven NPCs in MMORPGs and offers early insights into how such features are perceived by the WoW playerbase.

Quest of Aivengarde is a custom-built roleplaying game (RPG) that features a traditional dialogue tree alongside three alternative systems, each incorporating large language models (LLMs) to varying degrees — ranging from rephrasing to fully open-ended conversations. The systems are embedded in a shared game world with consistent narrative and challenges, allowing direct comparison of their design trade-offs. The prototype is intended as a modular testbed for future research, offering a flexible framework to experiment with dialogue models in controlled, interactive settings.

This project presents an evolutionary algorithm for texture generation that allows users to choose and manipulate camouflage patterns. The initial results of a pilot study provide some insight into usability and the users’ ability to replicate a target pattern. The result is an evaluation of gathered data showing user tendencies and how they engage with the system. These tendencies include significantly different completion times for target patterns varying in complexity. Additionally, participants mostly agreed that the tool is helpful for future games and objects other than camouflage skins. The findings suggest potential applications for artificial intelligence in enhancing user customization and design flexibility. Further research must address technical limitations and explore broader game industry implications.